Uncertainty Quantification for Underwater Object Segmentation

This dataset extends the Semantic Segmentation of Underwater Imagery: Dataset and Benchmark, adding an uncertainty evaluation component. To facilitate uncertainty analysis, the test set incorporates a comprehensive range of perturbations, inspired by Benchmarking Neural Network Robustness to Common Corruptions and Perturbations, applied at four intensity levels. These perturbations, which preserve the original ground truth labels, encompass variations in Brightness and Contrast (simulating diverse lighting and object coloration), Gaussian and Shot Noise (reflecting low-light and discrete light properties), and Impulse Noise (resulting from bit errors). Additionally, Defocus, Motion, and Zoom Blurs are included, along with Elastic Transformations, Pixelation from upscaling, and JPEG Compression artifacts. This enhanced dataset enables an in-depth evaluation of model robustness, providing valuable insights into performance under a wide range of challenging, real-world underwater conditions.